Module: Title Optimization

Trigger words: title, title, title optimization, create title, improve title

Goal: Generate and optimize academic paper titles according to IEEE/ACM/Springer/NeurIPS best practices.

Script Usage:

# 根据内容生成标题候选
uv run python $SKILL_DIR/scripts/optimize_title.py main.typ --generate

# 优化现有标题（按词边界删除无效词）
uv run python $SKILL_DIR/scripts/optimize_title.py main.typ --optimize

# 检查标题质量（评分 + 问题清单）
uv run python $SKILL_DIR/scripts/optimize_title.py main.typ --check

# 强制语言（默认自动检测）
uv run python $SKILL_DIR/scripts/optimize_title.py main.typ --check --lang en

Available flags:--generate / --optimize / --check / --lang {en,zh}。 The script does not provide interactive mode or --compare (the agent is not interactive); if you need to compare multiple titles, Run --check on each candidate individually and compare the scores.

Title Quality Standards (based on IEEE Author Center and top conferences/journals):

standard	weight	illustrate
Simplicity	25%	Delete "A Study of", "Research on", "Novel", "New"
Searchability	30%	Core terms (method + problem) within first 65 characters
length	15%	Best: 10-15 words (English) / 15-25 words (Chinese)
Specificity	20%	Specific method/problem name, avoid generalities
Normative	10%	Avoid uncommon abbreviations (except common abbreviations such as AI, LSTM, DNA, etc.)

Title generation workflow

Step 1: Content Analysis Extracted from the abstract/introduction:

• Research Question: What challenge is being addressed?
• Research Methods: What methods are proposed?
• Application areas: What application scenarios?
• Core Contribution: What are the main results? (optional)

Step 2: Keyword Extraction Identify 3-5 core keywords:

• Method keywords: "Transformer", "Graph Neural Network", "Reinforcement Learning"
• Question keywords: "Time Series Forecasting", "Fault Detection", "Image Segmentation"
• Field keywords: "Industrial Control", "Medical Imaging", "Autonomous Driving"

Step 3: Title Template Selection Common patterns for top conferences/journals:

model	Example (English)	Example (Chinese)	Applicable scenarios
Method for Problem	"Transformer for Time Series Forecasting"	"Transformer method for time series forecasting"	general studies
Method: Problem in Domain	"Graph Neural Networks: Fault Detection in Industrial Systems"	"Graph Neural Networks: Fault Detection in Industrial Systems"	Field specialization
Problem via Method	"Time Series Forecasting via Attention Mechanisms"	"Time series prediction based on attention mechanism"	Method focus
Method + Key Feature	"Lightweight Transformer for Real-Time Detection"	"Lightweight Transformer real-time detection method"	Performance focus

Step 4: Generate title candidates Generate 3-5 candidate titles with different focuses:

1. method-focused
2. problem focused
3. Application focused
4. Balanced type (recommended)
5. Concise variant

Step 5: Quality Score Each candidate title receives an overall score (0-100), breakdown scores for each criterion, and specific suggestions for improvement.

Title optimization rules

Delete invalid words:

English:

avoid using	reason
A Study of	Redundant (all papers are studies)
Research on	Redundant (all papers are research)
Novel/New	Implied by publication
Improved/Enhanced	Vague without specifics
Based on	Often unnecessary
Using/Utilizing	Can be replaced with prepositions

Chinese:

avoid using	reason
Research on...	Redundant (all papers are research)
Exploration of	redundant and unspecific
new/novel	Publication means novelty
Improved/Optimized	Not specific, need to explain how to improve
Based on	can be reduced to a direct statement

Example of recommended structure:

English:

Good: "Transformer for Time Series Forecasting in Industrial Control"
Bad:  "A Novel Study on Improved Time Series Forecasting Using Transformers"

Good: "Attention-Based LSTM for Multivariate Time Series Prediction"
Bad:  "An Improved LSTM Model Using Attention Mechanism for Prediction"

Chinese:

好：工业控制系统时间序列预测的Transformer方法
差：关于基于Transformer的工业控制系统时间序列预测的研究

好：注意力机制的多变量时间序列预测方法
差：基于注意力机制的改进型多变量时间序列预测模型研究

Keyword layout strategy

• First 65 characters (English) / First 20 characters (Chinese): The most important keywords (method + question)
• Avoid beginnings: Articles (A, An, The) / "About", "For"
• Preferred: nouns and technical terms over verbs and adjectives

Abbreviation usage guidelines

acceptable	Avoid in titles
AI, ML, DL	Obscure domain-specific acronyms
LSTM, GRU, CNN	Chemical formulas (unless very common)
IoT, 5G, GPS	Lab-specific abbreviations
DNA, RNA, MRI	Non-standard method names

Conference/Journal Special Requirements

IEEE Transactions：

• Avoid subscripted formulas
• Use Title Case (capitalize the first letter of the main word)
• Typical length: 10-15 words

ACM Conferences：

• Use more creative titles and colon subtitles
• Typical length: 8-12 words

Springer Journals:

• Prefer descriptive rather than creative, can be longer (up to 20 words)

NeurIPS/ICML:

• Be concise and powerful (8-12 words), method names usually stand out

Output format

English paper:

// ============================================================
// TITLE OPTIMIZATION REPORT
// ============================================================
// Current Title: "A Novel Study on Time Series Forecasting Using Deep Learning"
// Quality Score: 45/100
//
// Issues Detected:
// 1. [Critical] Contains "Novel Study" (remove ineffective words)
// 2. [Major] Vague method description ("Deep Learning" too broad)
//
// Recommended Titles (Ranked):
// 1. "Transformer-Based Time Series Forecasting for Industrial Control" [Score: 92/100]
// 2. "Attention Mechanisms for Multivariate Time Series Prediction" [Score: 88/100]
//
// Suggested Typst Update:
// #align(center)[
//   #text(size: 18pt, weight: "bold")[
//     Transformer-Based Time Series Forecasting for Industrial Control
//   ]
// ]
// ============================================================

Chinese Paper:

// ============================================================
// 标题优化报告
// ============================================================
// 当前标题："关于基于深度学习的时间序列预测的研究"
// 质量评分：48/100
//
// 推荐标题（按评分排序）：
// 1. "工业控制系统时间序列预测的Transformer方法" [评分: 94/100]
// 2. "多变量时间序列预测的注意力机制研究" [评分: 89/100]
// ============================================================

Typst title setting example:

English paper:

#align(center)[
  #text(size: 18pt, weight: "bold")[
    Transformer-Based Time Series Forecasting for Industrial Control
  ]
]

Chinese Paper:

#align(center)[
  #text(size: 18pt, weight: "bold", font: "Source Han Serif")[
    工业控制系统时间序列预测的Transformer方法
  ]

  #v(0.5em)

  #text(size: 14pt, font: "Times New Roman")[
    Transformer-Based Time Series Forecasting for Industrial Control Systems
  ]
]

Reference resources:

• IEEE Author Center
• Royal Society Blog on Title Optimization